Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding and evaluating these datasets is difficult, and the failure modes of LLM-generated data are still not well understood. Specifically, the data can be repetitive in surprising ways, not only semantically but also syntactically and lexically. We present LinguisticLens, a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of LLM-generated datasets. LinguisticLens clusters text along syntactic, lexical, and semantic axes. It supports hierarchical visualization of a text dataset, allowing users to quickly scan for an overview and inspect individual examples. The live demo is available at shorturl.at/zHOUV.
@article{arxiv.2305.11364,
title = {Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models},
author = {Emily Reif and Minsuk Kahng and Savvas Petridis},
journal= {arXiv preprint arXiv:2305.11364},
year = {2023}
}